Week 11 Lecture Notes - Judgement and Reasoning PDF

PSYC2112/5262 I FOUNDATIONAL PERSPECTIVES: COGNITION Lecture 10. Judgement and Reasoning Dr Jonas Chan We acknowledge the Traditional Owners of country throughout Australia and recognise their continuing connection to land, waters and culture. We pay our respects to their Elde...

PSYC2112/5262 I FOUNDATIONAL PERSPECTIVES: COGNITION Lecture 10. Judgement and Reasoning Dr Jonas Chan We acknowledge the Traditional Owners of country throughout Australia and recognise their continuing connection to land, waters and culture. We pay our respects to their Elders past, present and emerging. We recognise those whose ongoing effort to protect and promote Aboriginal and Torres Strait Islander cultures will leave a lasting legacy for future Elders and leaders. 2 LECTURE SERIES BREAKDOWN Week Topics Topic 1 Introduction to Memory and Cognition Topic 2 Learning & Critical Thinking Topic 3 Attention Topic 4 Short-term and working memory Topic 5 Long-term memory Topic 6 Everyday Memory Break Week Topic 7 Conceptual Knowledge Topic 8 Language & Reading Topic 9 Problem Solving & Creativity Topic 10 Judgement & Reasoning Topic 11 Decision Making Topic 12 Review: Introduction to Cognition TODAY 1. Making Judgements 2. Deductive Reasoning MAKING JUDGEMENTS What kinds of reasoning “traps” do Judgement & people get into when making Reasoning judgments? What is the evidence that people Making sometimes make decisions that are Judgements not in their best interests? Deductive Reasoning How do emotions influence decision-making? MAKING JUDGEMENTS Judgements: Judgement & The process of forming an opinion Reasoning or conclusion. Decisions: Making The process of making choices Judgements between alternatives. Deductive Reasoning Reasoning: The process of drawing conclusions based on evidence All these components of thinking are related. But we will discuss them separately. INDUCTIVE REASONING Inductive Reasoning: Observation: Reasoning that is based on All the dogs I’ve seen in Melbourne Judgement & observation or reaching are completely black. When I visited Reasoning conclusions from evidence. Sydney the dogs I saw there were black too. Science is based on inductive Making Conclusion: reasoning. I think it is a pretty good bet that all Judgements dogs are black. 1. Have an idea. Deductive 2. Collect data. Reasoning Observation: 3. Conclusions are drawn. Here in Melbourne, I’ve seen the sun rise every morning. The conclusions we draw from inductive reasoning are probably Conclusion: true, not definitely true. I think the sun is going to rise tomorrow morning. INDUCTIVE REASONING Factors that contribute to the Observation: strength of an inductive argument: All the dogs I’ve seen in Melbourne Judgement & are completely black. When I visited Reasoning 1. Representativeness of Perth the dogs I saw there were observations. black too. 2. Number of observations. Making Conclusion: 3. Quality of evidence. I think it is a pretty good bet that all Judgements dogs are black. We use inductive reasoning Deductive everyday. Reasoning Observation: Here in Sydney, I’ve seen the sun Anytime we make a prediction rise every morning. about what will happen based on our observations about what has Conclusion: happened in the past, we are using I think the sun is going to rise inductive reasoning. tomorrow morning. HEURISTICS Heuristics: “Rules of thumb” that are likely to provide the correct answer to a Judgement & problem but are not fool proof. Reasoning Two more commonly used heuristics include the availability heuristic and the representativeness heuristic. Making Judgements > cognitive shortcuts Deductive - Reasoning HEURISTICS Which cause of death in each row is more likely, A or B? Judgement & Reasoning A B Making Homicide Appendicitis Judgements Drowning Auto-train collision Deductive Asthma Botulism Reasoning Asthma Tornado Appendicitis Pregnancy AVAILABILITY HEURISTIC Events more easily remembered are judged as being more probable than those less easily remembered (Tversky & Kahneman, 1973). Judgement & Reasoning The availability heuristic can mislead us into reaching the wrong conclusion when less frequently occurring events stand out in our memory. Making Judgements More Likely Less Likely Percent of Participants Picking Deductive Less Likely Reasoning Homicide (20) Appendicitis 9 Drowning (5) Auto-train collision 34 Asthma (920) Botulism 41 Asthma (20) Tornado 58 Appendicitis (2) Pregnancy 83 ILLUSORY CORRELATIONS Correlation appears to exist, but either does not exist or is much weaker than assumed. Judgement & Reasoning A girl wins money on a scratch lottery ticket when using one specific coin. She now scratches all her tickets with that lucky coin. Making A man hears about a car accident on George Street, so he avoids George Judgements Street again for fear that the street is an accident hot spot. Deductive Reasoning A woman spills coffee on herself after leaving a coffee shop on Elizabeth Street. She later refuses to go there because she believes the shop is the reason that she spilt the coffee. -Skinner Pigeon experiment - STEREOTYPES An oversimplified generalization about a group or class of people that often focuses on the negative. Judgement & Reasoning All politicians only think of personal gain and benefit. A stereotype about the characteristics of a group may lead people to Making pay attention to behaviours associated with that stereotype. Judgements Deductive Reasoning Are stereotypes related to the availability heuristic? It could be, because selective attention to the stereotypical behaviours makes these behaviours more “available” (Chapman & Chapman, 1969; Hamilton, 1981). MAKING JUDGEMENTS Assimilation: Fitting new data (a person or situation) within existing theories Judgement & (stereotypes about the person or situation) Reasoning Accommodation: Changing the stereotypes to incorporate the new data Making Judgements We are resistant to changing our stereotypes. It’s more economical to assimilate than to accommodate. Deductive Reasoning We bend our new experiences so that they fit within our existing stereotypes: Biased assimilation ILLUSORY CAUSATIONS Causation appears to exist, but either does not exist or is much weaker than assumed. Judgement & Reasoning Cause density bias: The tendency to overestimate the relationship between a cue and outcome when the cue (“cause”) occurs frequently. Making E.g. Echinacea tea 4 times daily as a remedy for cold and flu. Judgements Outcome density bias: Deductive The tendency to overestimate the relationship between a cue and Reasoning outcome when the outcome occurs frequently. E.g. Spontaneous remission of back pain leads to the causal illusion that alternative medicine can treat back pain. Once an illusory causal judgement has been established, it can be Review paper on illusions of difficult to replace this with new information, even when the new causality information better explains the outcome (Yarritu et al, 2015). REPRESENTATIVENESS HEURISTIC The availability heuristic is related to how often we expect events to occur, the representativeness heuristic is related to the idea that Judgement & people often make judgments based on how much one event Reasoning resembles another event. Representativeness Heuristic: Making The probability that A is a member of class B can be determined by Judgements how well the properties of A resembles the properties we usually associate with class B. Deductive Reasoning REPRESENTATIVENESS HEURISTIC You were probably influenced by the fact that the description of Robert matches your conception of what a librarian is like. Judgement & But you might have been ignoring another important source of information… Reasoning Base Rate: The relative proportion of different classes in the population. Making When this experiment was carried out, there were many more male farmers than Judgements male librarians in the United States (Tversky & Kahneman, 1974). Deductive In our example, if Robert was randomly chosen from the population, it is much Reasoning more likely that he was a farmer. BASE RATE NEGLECT A “In a group of 100 people, there What happens when the are 70 lawyers and 30 engineers. participants are given the base rate What is the chance that if we Judgement & information? pick one person from the group Reasoning at random that the person will be When presented with the base rate an engineer?” information, as in A, participants Making correctly guess the odds. B Judgements “Jack is a 45-year-old man. He is But adding the description, such as married and has four children. Deductive in B, participants increase their He is generally conservative, Reasoning estimate that a random person is an careful, and ambitious. He engineer. shows no interest in political and social issues and spends most of his free time on his many Base rate neglect! hobbies, which include home carpentry, sailing, and mathematical puzzles.” THE INVERSE BASE RATE EFFECT Medin and Edelson (1988) conducted an experiment where participants were tasked to diagnose diseases Judgement & on the basis of symptom pairs. Reasoning All patients with symptom A and B had disease O1. Making All patients with symptoms A and C had disease O2. Judgements Disease O1 (common) occurred three times more Deductive often than Disease O2 (rare). Reasoning - After learning the contingencies, participants were shown symptoms B and C together (transfer test), and asked to diagnose the disease. Participants tended to diagnose disease O2, even though O1 was more common – inverse base rate effect. “When you hear hoofs, think horse, not zebra.” MAKING JUDGEMENTS Judgement & Reasoning Making Judgements Deductive Reasoning MAKING JUDGEMENTS Judgement & Conjunction Rule: Reasoning Probability of conjunction two events cannot be higher than the probability of the single constituents Making Judgements Because feminist bank tellers are a Deductive subset of bank tellers, it is always Reasoning more likely that someone is a bank teller than a feminist bank teller. - - MAKING JUDGEMENTS Law of large numbers: the larger the number of individuals randomly drawn from a population, the Judgement & more representative the resulting group will be of the entire population. Reasoning Making Judgements Deductive Reasoning - -less representative of the population When subjects were asked this question in an experiment (Tversky & Kahneman, 1974), 22 percent picked the larger hospital, 22 percent picked the smaller hospital, and 56 percent stated that there would be no difference. ATTITUDES THAT AFFECT JUDGEMENTS Myside Bias: Tendency for people to generate and evaluate evidence and test their Judgement & hypotheses in a way that is biased toward their own opinions and attitudes. Reasoning Making Lord and coworkers (1979) Judgements Had those in favor of capital punishment and those Deductive against it read the same article. Reasoning Those in favor found the article convincing. Those against found the article unconvincing. ATTITUDES THAT AFFECT JUDGEMENTS Confirmation Bias: Tendency to selectively look for information that conforms to our hypothesis Judgement & and overlook information that argues against it (Wason, 1960). Reasoning Making Judgements 2 4 6 Deductive Reasoning CONFIRMATION BIAS Judgement & Reasoning Making Judgements Deductive Reasoning CONFIRMATION BIAS numbers. chick wrong again > Doesn't - Judgement & Reasoning Making Judgements Deductive Reasoning  CONFIRMATION BIAS Judgement & Reasoning Making Judgements Deductive Reasoning find correct ones helps us. Getting answers > - wrong , EVALUATING FALSE EVIDENCE Even if you reason without error, it is still possible to arrive at incorrect conclusions – if the facts are wrong to begin with. Judgement & Reasoning People are not always diligent about evaluating evidence, and rely heavily on inaccurate information. Wineburg et al. (2016) showed a real post from a photo-sharing website to Making students of malformed daisies with the claim that the flowers had “nuclear Judgements birth defects” from Japan’s Fukushima Daiichi nuclear disaster, but provided no sources or anything else to back up the claim. Deductive Reasoning When asked if the post provided strong evidence of the radioactive conditions near the nuclear plant, 80% of students were inclined to believe the evidence. Even though no other information was provided. Perhaps people tend to believe information they are given if they are not given/do not seek out the resources to evaluate it? Not always! backfire effect > - The THE BACKFIRE EFFECT Nyhan & Reifler (2010) study based on the misperception that Iraq was hiding weapons of mass destruction (WMDs)—a statement disseminated by the Bush administration in order to justify the subsequent U.S. invasion of Iraq. Judgement & Reasoning Participants shown a mock news story suggesting that there were WMDs in Iraq. One group of participants provided with a correction that no WMDs were Making ever found. Judgements Participants then shown statement “Immediately before the U.S. invasion, Iraq Deductive had an active weapons of mass destruction program, the ability to produce Reasoning these weapons, and large stockpiles of WMDs, but Saddam Hussein was able to hide or destroy these weapons right before U.S. forces arrived.” Participants’ evaluation appeared to differ both depending on political affiliations and whether they had been corrected. THE BACKFIRE EFFECT Participants then shown statement “Immediately before the U.S. invasion, Iraq had an active weapons of mass destruction program, the ability to produce these weapons, and large stockpiles of WMDs, but Saddam Hussein was able to Judgement & hide or destroy these weapons right before U.S. forces arrived.” Reasoning Among participants who described themselves as very liberal, those who received the correction were more likely to disagree with the statement than Making those who did not receive the correction. Judgements The correction reduced the misperception. Deductive The correction had no impact on moderate-liberal and centrist participants. Reasoning Moderate-conservative and very conservative participants were more likely to agree with the statement if they were given the correction. Participants’ viewpoints could become stronger when faced with corrective facts opposing their viewpoint – the backfire effect. SUMMARY SO FAR Judgement & Reasoning Making Judgements Deductive Reasoning SUMMARY SO FAR Judgement is the process of forming an opinion or a conclusion. Inductive reasoning is based on observation or reaching conclusions from evidence. Judgement & Reasoning Heuristics are rules of thumb that are likely to provide the correct answer to a problem but are not fool-proof. Availability heuristic Making Representativeness heuristic Judgements Overreliance on heuristics may lead to: Illusory correlations and stereotypes Deductive Illusory causations Reasoning Incorrect judgment of base rates Cognitive biases may hinder our ability to gather evidence and distort our judgement Myside bias Confirmation bias Backfire effect DEDUCTIVE REASONING Deductive Reasoning: Determining whether a conclusion logically follows from premises. Judgement & Reasoning Syllogism: Two statements called premises followed by a third statement called a conclusion. Making Judgements Let’s consider a categorical syllogism, where we describe the relation between two categories using all, no, or some. Deductive Reasoning Does the conclusion in syllogism 1follow from the two premises? - Syllogism 1 Premise 1: All birds are animals. (All A are B) Premise 2: All animals eat food. (All B are C) Conclusion: Therefore, all birds eat food. (All A are C) SYLLOGISMS A syllogism is valid if the conclusion follows logically from its two premises. - Judgement & If two premises of a valid syllogism are true, the syllogism’s conclusion must be Reasoning true. The word true is hard to define, but let’s assume it means in accordance with Making reality. Judgements Syllogism 2 is valid but not true. Deductive Reasoning Validity does not equal truth! but not factually. > Can - accept it logically Syllogism 2 Premise 1: All birds are animals. (All A are B) Premise 2: All animals have four legs. (All B are C) Conclusion: Therefore, all birds have four legs. (All A are C) SYLLOGISMS Syllogisms can be invalid even though each of the premises and the conclusion seem reasonable. Judgement & Reasoning Is the reasoning behind syllogism 3 valid? Making Judgements Deductive Reasoning Syllogism 3 Premise 1: All of the students are tired. (All A are B) Premise 2: Some tired people are irritable (Some B are C) Conclusion: Therefore, some students are irritable. (Some A are C) SYLLOGISMS Syllogism 3 may refer to Judgement & different groups of tired people! Reasoning Belief Bias: The tendency to think a A B C Making syllogism is valid if its conclusion Judgements is believable. Deductive Reasoning Evans et al. (1983) Syllogism 3 Premise 1: All of the students are tired. (All A are B) Premise 2: Some tired people are irritable (Some B are C) Conclusion: Therefore, some students are irritable. (Some A are C) SYLLOGISMS be a Syllogism 3 may refer to different groups of tired people! Belief Bias: The tendency to think a. Judgement & Reasoning A B C Making syllogism is valid if its conclusion Judgements is believable. Deductive What makes syllogism 3 easy to Reasoning believe and syllogism 4 easy to invalidate? Evans et al. (1983) Syllogism 4 Premise 1: All of the students live in Tucson. (All A are B) Premise 2: Some people who live in Tucson are millionaires. (Some B are C) Conclusion: Therefore, some of the students are millionaires. (Some A are C) MENTAL MODEL On a pool table there is a black ball directly above the cue ball. The green ball is on Mental Model: the right side of the cue ball, A specific situation represented in a Judgement & and there is a red ball person’s mind that can be used to help Reasoning between them. If I move so determine the validity of syllogisms in deductive reasoning. the red ball is between me and the black ball, the cue ball Making Create a model of a situation. is to the of my line of sight. Judgements Generate tentative conclusions about Deductive model. Reasoning Look for exceptions to falsify model. If there are no more exceptions, determine validity of syllogism. MENTAL MODEL On a pool table there is a black ball directly above the cue ball. The green ball is on Mental Model: the right side of the cue ball, A specific situation represented in a Judgement & and there is a red ball person’s mind that can be used to help Reasoning between them. If I move so determine the validity of syllogisms in deductive reasoning. the red ball is between me and the black ball, the cue ball Making Create a model of a situation. is to the of my line of sight. Judgements Generate tentative conclusions about Deductive model. Reasoning Look for exceptions to falsify model. If there are no more exceptions, determine validity of syllogism. 1 MENTAL MODEL On a pool table there is a black ball directly above the cue ball. The green ball is on Mental Model: the right side of the cue ball, A specific situation represented in a Judgement & and there is a red ball person’s mind that can be used to help Reasoning between them. If I move so determine the validity of syllogisms in deductive reasoning. the red ball is between me and the black ball, the cue ball Making Create a model of a situation. is to the of my line of sight. Judgements Generate tentative conclusions about Deductive model. Reasoning Look for exceptions to falsify model. If there are no more exceptions, determine validity of syllogism. 1 MENTAL MODEL On a pool table there is a black ball directly above the cue ball. The green ball is on Mental Model: the right side of the cue ball, A specific situation represented in a Judgement & and there is a red ball person’s mind that can be used to help Reasoning between them. If I move so determine the validity of syllogisms in deductive reasoning. the red ball is between me and the black ball, the cue ball Making Create a model of a situation. is to the of my line of sight. Judgements Generate tentative conclusions about Deductive model. Reasoning Look for exceptions to falsify model. If there are no more exceptions, determine validity of syllogism. 2 1 MENTAL MODEL FOR SYLLOGISMS Create a model of a situation. Judgement & Generate tentative conclusions about model. Reasoning Look for exceptions to falsify model. Making Premise 1. None of the artists are beekeepers. Judgements Premise 2. All of the beekeepers are chemists. Conclusion. Some of the chemists are not artists. Deductive Reasoning Imagine that we are visiting a meeting of artists, beekeepers and chemists. They all wear different hats. MENTAL MODEL FOR SYLLOGISMS Premise 1. None of the artists are beekeepers. Premise 2. All of the beekeepers are chemists. Judgement & Reasoning Making Judgements Deductive Reasoning MENTAL MODEL FOR SYLLOGISMS Premise 1. None of the artists are beekeepers. Premise 2. All of the beekeepers are chemists. Judgement & Create a model of a situation. Reasoning Generate tentative conclusions about model. Look for exceptions to falsify model. Making Exception Model 1. No chemists are artists. Judgements Deductive Reasoning MENTAL MODEL FOR SYLLOGISMS Premise 1. None of the artists are beekeepers. Premise 2. All of the beekeepers are chemists. Judgement & Create a model of a situation. Reasoning Generate tentative conclusions about model. Look for exceptions to falsify model. Making Exception Model 1. No chemists are artists. Judgements Deductive Reasoning MENTAL MODEL FOR SYLLOGISMS Premise 1. None of the artists are beekeepers. Premise 2. All of the beekeepers are chemists. Judgement & Create a model of a situation. Reasoning Generate tentative conclusions about model. Look for exceptions to falsify model. Making Exception Model 2. Some of the chemists are not artists. Judgements Deductive Reasoning MENTAL MODEL FOR SYLLOGISMS Premise 1. None of the artists are beekeepers. Premise 2. All of the beekeepers are chemists. Judgement & Create a model of a situation. Reasoning Generate tentative conclusions about model. Look for exceptions to falsify model. Making Exception Model 2. Some of the chemists are not artists. Judgements - Deductive Reasoning MENTAL MODEL FOR SYLLOGISMS Judgement & Conclusion. Some of the chemists are not artists. Reasoning Making Judgements Deductive Reasoning CONDITIONAL SYLLOGISMS Have two premises and a conclusion like categorical syllogisms, but the first premise has the form “If... then.” Judgement & Reasoning All begin with “If p, then q”: Making Second Judged Syllogism Conclusion Is It Valid? Correctly? Judgements Premise Syllogism 1: p Therefore, q Yes 97% Deductive Modus ponens Reasoning Syllogism 2: Modus Not q Therefore, not p Yes 60% tollens Affirmation of the Syllogism 3 consequent q Therefore, p No 40% - Syllogism 4 Derail Not p Therefore, not q No 40% - of the antecedent. CONDITIONAL SYLLOGISMS Judgement & Reasoning This form of syllogism—called modus ponens, which is Latin for (roughly Conditional Syllogism 1 translated) “the way that affirms by If I study, I’ll get a good grade. Making affirming”—is valid. I studied. Judgements The conclusion follows logically from the Therefore, I’ll get a good grade. two premises. Deductive Reasoning CONDITIONAL SYLLOGISMS Judgement & Reasoning This type of syllogism, called modus tollens (for “the way that denies by Conditional Syllogism 2 denying”), is valid. If I study, I’ll get a good grade. Making I didn’t get a good grade. Judgements Therefore, I didn’t study. Deductive Reasoning  CONDITIONAL SYLLOGISMS Judgement & Reasoning This form of syllogism is called affirmation of the consequent. The conclusion in this syllogism (“I Conditional Syllogism 3 Making studied”) is not valid because even if you If I study, I’ll get a good grade. Judgements didn’t study, it is still possible that you I got a good grade. could have received a good grade. Deductive Therefore, I studied. Reasoning Perhaps the exam was easy, or maybe you already knew the material. * There is a difference of something being true and valid. CONDITIONAL SYLLOGISMS Judgement & Reasoning This form of syllogism is called denial of the antecedent. Conditional Syllogism 4 The conclusion of this syllogism (I If I study, I’ll get a good grade. Making didn’t get a good grade) is not valid. Judgements I didn’t study. Therefore, I didn’t get a good grade. Deductive Again, the exam could have been really easy for example. Reasoning THE WASON FOUR-CARD PROBLEM Effect of using real-world items in a conditional reasoning problem. Judgement & Determine minimum number of cards to turn over to test: Reasoning If there is a vowel on one side, then there is an even number on the other side. Making Judgements Deductive Reasoning THE WASON FOUR-CARD PROBLEM Finding an even number on the other side of E conforms to the rule. Judgement & Finding an odd number on the other side of E Reasoning falsifies rule. 46 percent of subjects indicated that in addition Making to the E, the 4 would need to be turned over. Judgements But the rule doesn’t mention consonants and Deductive finding a vowel on the other side of the 4 tells us Reasoning it works in this case. Finding a vowel on the other side of the 7 falsifies the rule. Falsification Principle: To test a rule, it is necessary to look for situations that would falsify the rule. THE WASON FOUR-CARD PROBLEM Real world version of the four-card problem. Judgement & Performance improves in real-world terms (Griggs & Cox, 1982): Reasoning Making Judgements Deductive Reasoning THE WASON FOUR-CARD PROBLEM 73% of participants provided the correct Judgement & response, compared to 0 answering the Reasoning abstract condition correctly. Why? Making Involves familiar regulations (Griggs & Cox, Judgements 1982). Deductive Participants applied the schema of Reasoning permissions to the problem to better focus on the cards required to test that schema. (Cheng & Holyoak, 1985). People are on the lookout for cheaters because this confers an evolutionary advantage (1992). CAN NON-HUMAN ANIMALS REASON? Research into the reasoning abilities of animals has often followed attempts to Judgement & teach animals to use language. Reasoning Premack (1983) reported that language- trained chimps can succeed on some reasoning tasks that chimps who were Making never language-trained cannot. Judgements Premack: non-language trained chimps Deductive use only an imaginal code (related to Reasoning visual properties of objects) while language-trained chimps can use an abstract code. Using the abstract code can help chimps reason. CAN NON-HUMAN ANIMALS REASON? Not all reasoning tasks require abstract code. Premack (1983) Judgement & A chimp was shown two containers positioned at opposite ends of a Reasoning room. Person A placed an apple under one container and a banana in the other with the chimp watching. Making The chimp was then removed from the room by Person B. Judgements The chimp was returned to the room where she was shown person A standing in the centre of the room eating an apple or a banana. Deductive Both people then left the room to allow the chimp to choose a container. Reasoning All chimps, both language trained and untrained reliably chose the container with the fruit that was not eaten by person A. Thus, prior language training was unimportant for this task. We find the same result with children over 4 years of age. CAN NON-HUMAN ANIMALS REASON? Not all reasoning tasks require abstract code. Premack (1983) Judgement & An analogy is a statement of the form “A is to B as C is to D”, and an ability Reasoning to solve an analogy is usually assessed by offering various alternatives for D. Making An imaginal code is not sufficient in solving analogies because analogies Judgements are not based on physical similarities. Deductive Chimpanzees appear to require language to solve analogies. Reasoning Perhaps language is required to understand abstract concepts. However, language may also simply allow chimpanzees to be better test takers. Premack claimed that only primates are capable of learning an abstract code. Difficult to test this claim, especially in light of language learning in non- primates. SUMMARY SO FAR Deductive reasoning involves determining whether a conclusion logically follows from its premises. Judgement & Reasoning A syllogism consists of two premises followed by a conclusion. The reader is required to determine whether the syllogism is true and valid. We can use the mental model approach to help us validate a syllogism. Making Judgements Two examples of syllogisms are: Categorical Deductive Conditional Reasoning The belief bias is the tendency for people to think a syllogism is valid if its conclusion is believable. The Wason 4 card problem demonstrates several syllogisms in real world conditions. Premack (1983) claimed that language is a requirement for abstract thought. LECTURE SERIES BREAKDOWN Week Topics Topic 1 Introduction to Memory and Cognition Topic 2 Learning & Critical Thinking Introduction to Cognition Topic 3 Attention Topic 4 Short-term and working memory Topic 5 Long-term memory Topic 6 Everyday Memory Break Week Topic 7 Conceptual Knowledge Topic 8 Language & Reading Topic 9 Problem Solving & Creativity Topic 10 Judgement & Reasoning Topic 11 Decision Making Topic 12 Review: Introduction to Cognition PSYC2112/5262 FOUNDATIONAL PERSPECTIVES: COGNITION Week 11 Tutorial Judgement and Reasoning  ACKNOWLEDGEMENT OF COUNTRY ACAP acknowledges the traditional owners of country throughout Australia and recognize their continuing connection to land, waters and culture. We pay our respects to their Elders past, present and emerging. We recognize those whose ongoing effort to protect and promote Aboriginal and Torres Strait Islander cultures will leave a lasting legacy for future Elders and leaders.  Week 1: Unit orientation and data collection Week 2: Learning and critical thinking & introduction to A1 A3 WEEKLY EXAM Week 3: Attention A3 WEEKLY EXAM Week 4: Short-term and working memory & Assessment 2 Part 1 A3 WEEKLY EXAM Week 5: Long-term memory & Assessment 2 Part 2 A1 DUE A3 WEEKLY EXAM Week 6: Everyday memory A3 WEEKLY EXAM Week 7: Break week Week 8: Conceptual knowledge A3 WEEKLY EXAM Week 9: Language and reading A3 WEEKLY EXAM Week 10: Problem solving and creativity A2 DUE A3 WEEKLY EXAM TODAY’S TOPIC Week 11: Judgement and reasoning A3 WEEKLY EXAM Week 12: Decision making A3 WEEKLY EXAM Week 13: Review and reflection Outline ▪ Likelihood task ▪ Heuristics ▪ Verifying information using syllogisms ▪ Confirmation bias LIKELIHOOD AND HEURISTICS LIKELIHOOD TASK Take out a piece of paper. In the next few slides, you will be asked some questions related to the judgement of statistics. Please write down your answers on your piece of paper. 1. Rafael Nadal was the winner of the Australian Open and the French Open in 2022. Suppose Nadal reaches the Australian Open final in 2023. Please rank the following outcomes from most to least likely: A. Nadal will win the match. B. Nadal will lose the first set. 60 C. Nadal will lose the first set but win the match. D. Nadal will win the first set but lose the match. 2. Bill is 34 years old. He is intelligent, but unimaginative, compulsive, and generally lifeless. In school, he was strong in mathematics but weak in social studies and humanities. Rank the below in order of likelihood: A. Bill is a physician who plays poker for a hobby. B. Bill is an architect. C. Bill is an accountant. D. Bill plays jazz for a hobby. E. Bill surfs for a hobby. 60 F. Bill is a reporter. G. Bill is an accountant who plays jazz for a hobby. H. Bill climbs mountains for a hobby. 3. In a study 1000 people were tested. Among the participants there were 995 nurses and 5 doctors. Paul is a randomly chosen participant of this study. Paul is 34 years old. He lives in a beautiful home in a posh suburb. He is well spoken and very interested in politics. He invests a lot of time in his career. What is the probability in % that Paul is a doctor? 60 1. Rafael Nadal was the winner of the Australian Open and the French Open in 2022. Suppose Nadal reaches the Australian Open final in 2023. Please rank the following outcomes from most to least likely: A. Nadal will win the match. Which did you pick as B. Nadal will lose the first set. more likely? Why? C. Nadal will lose the first set but win the match. D. Nadal will win the first set but lose the match. Rafael Nadal was the winner of the Australian Open and the French Open in 2022. Suppose Nadal reaches the Australian Open final in 2023. Please rank the following outcomes from most to least likely: Nadal lose A. Nadal will win the match. (1.7) + win B. Nadal will lose the first set. (2.7) C. Nadal will lose the first set but win the match. (2.2) D. Nadal will win the first set but lose the match. (3.5) C rated as more likely even though by definition, C cannot be more likely than B. 2. Bill is 34 years old. He is intelligent, but unimaginative, compulsive, and generally lifeless. In school, he was strong in mathematics but weak in social studies and humanities. Rank the below in order of likelihood: A. Bill is a physician who plays poker for a hobby. B. Bill is an architect. Which did you pick as C. Bill is an accountant. more likely? Why? D. Bill plays jazz for a hobby. E. Bill surfs for a hobby. F. Bill is a reporter. G. Bill is an accountant who plays jazz for a hobby. H. Bill climbs mountains for a hobby. Bill is 34 years old. He is intelligent, but unimaginative, compulsive, and generally lifeless. In school, he was strong in mathematics but weak in social studies and humanities. Rank the below in order of likelihood: A. Bill is a physician who plays poker for a hobby. B. Bill is an architect. C. Bill is an accountant. (A) D. Bill plays jazz for a hobby. (B) E. Bill surfs for a hobby. F. Bill is a reporter. G. Bill is an accountant who plays jazz for a hobby. (A&B) H. Bill climbs mountains for a hobby. G should by definition be less likely than either C or D. However, people often choose G as more likely than D. 3. In a study 1000 people were tested. Among the participants there were 995 nurses and 5 doctors. Paul is a randomly chosen participant of this study. Paul is 34 years old. He lives in a beautiful home in a posh suburb. He is well spoken and very interested in politics. He invests a lot of time in his career. What is the probability in % that Paul is a doctor? In a study 1000 people were tested. Among the participants there were 995 nurses and 5 doctors. Paul is a randomly chosen participant of this study. Paul is 34 years old. He lives in a beautiful home in a posh suburb. He is well spoken and very interested in politics. He invests a lot of time in his career. What is the probability in % that Paul is a doctor? There are 995 nurses and 5 doctors in the group, so Paul has a 0.5% probability of being a doctor. REPRESENTATIVENESS HEURISTIC The questions on the previous slides demonstrates that we are likely to use the prototypes that exist in our minds to make judgements about statistics, rather than the relevant information. Chase is a tall and Professional basketball athletic man living in player Australia. He likes listening to rap music and he dances as a hobby. What is his likely profession? Salesperson Consider the base rate! AVAILABILITY HUERISTIC Estimating the likelihood or frequency of an event based on how easily All of the relevant examples or instances come information to mind → If something is readily available in memory, it must be more common or probable What information is most “available” to you, and what we’re probably making decisions on! AVAILABILITY HEURISTIC A survey conducted in the U.S. in 2010 found that the most feared ways to die were: 1. Terrorist attack 2. Shark attack 3. Airplane crash Why do you think these were rated so highly? Is there a high 4. Being murdered likelihood that these could 5. Natural disaster occur? 6. Falling AVAILABILITY HEURISTIC A survey conducted in the U.S. in 2010 found that the most feared ways to die were: 1. Terrorist attack (1 in 9.3 million) 2. Shark attack (1 in 11.5 million) 3. Airplane crash (1 in 200,000) 4. Being murdered (1 in 500,000) 5. Natural disaster (1 in 3000) 6. Falling (1 in 21,000) AVAILABILITY HEURISTIC Actual leading causes of death in the U.S. : 1. Tobacco (1 in 5) 2. Poor diet/physical inactivity (1 in 6) 3. Alcohol consumption (1 in 28) 4. Microbial agents (1 in 32) 5. Toxic agents (1 in 43) 6. Motor vehicle crashes (1 in 56) People tended to misjudge probable causes of death likely due to those causes being more available in the mind. What do you think is contributing to the availability heuristic? VERIFYING INFORMATION Question 1. Your company has instituted a new rule, where employees working at the office for over 20 hours a week must be vaccinated. The cards below show the number of hours worked at the office on one side, and vaccination status on the other. You must not allow any employee who works over 20 hours a week who has not been vaccinated into the building. 9 27 Vaccinated Not Vaccinated Which cards must you turn over to make sure that all employees are following the rules? Question 2. You are serving at a bar and have to enforce the rule that if a person is drinking beer, they must be over 18 years of age. The four cards below have information about people sitting at a table. One side of the card tells you what a person is drinking and the other side tells their age. Which card or cards must you turn over to see if the rule is being broken? Beer Tea 25 16 Which cards must you turn over to make sure that all employees are following the rules? Question 3. Assume that each card has a letter on one side and a number on the other. Rule: if a card has a D on one side, then it has a 7 on the other Find ways of validating and ensuring the rules are not D F 7 5 invalidated. To check whether the rule is true of these cards, which cards do you need to turn over? CONFIRMATION BIAS CONFIRMATION BIAS Objective: There are 3 numbers, and these numbers obey a particular rule. Your task is to try and figure out what the rule is. The way you can figure this out is by proposing your own set of 3 numbers and when it is consistent with the rule, I will say yes, and if inconsistent, I will say no. Based on this process, you have to figure out what the rule is. 2, 4, 8 What is the rule? CONFIRMATION BIAS Deductive reasoning requires the reasoner to find evidence that disproves the rule. People often avoid doing that, and instead continue only looking for evidence that supports their idea. https://www.youtube.com/watch?v=vKA4w2O61Xo&feature=emb_logo  Key Reminders Tutorial topic for next week: ▪ Decision making Any questions? Reminders: ▪ Complete after class activities ▪ Complete weekly exam Thank you!